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I build data pipelines and cloud warehouses that replace manual reporting, connect fragmented systems, and give teams data they can actually trust. My core work is GCP data engineering: BigQuery, Airflow, dbt, Python, SQL, ETL/ELT, API integrations, and data warehouse optimization. I also work across AWS, PostgreSQL, Redshift, and mixed legacy environments. I can help with: • AWS/GCP data infrastructure and cross-system integrations • ETL/ELT pipeline development and optimization • BigQuery data warehouse design, migration, and cost optimization • Airflow orchestration and production pipeline reliability • dbt transformations and analytics-ready data models • REST/API integrations and automated data ingestion • Legacy database and ETL migration to GCP • Python and SQL data engineering The technical work is only part of the job. I care about whether the pipeline is reliable, observable, maintainable, and actually useful to the people consuming the data. If you're dealing with unreliable pipelines, manual data work, slow reporting, expensive warehouse queries, or disconnected systems, send me the current setup and what you're trying to achieve. I'll give you a direct assessment of the best way to solve it.

I build data warehouses that run faster and cost less. One client went from a $4,000 monthly bill to $40, with query times dropping from 84 hours to 2. I help SaaS companies, agencies, and data-heavy startups move off slow, expensive setups and onto Snowflake, BigQuery, and dbt pipelines they can actually trust. WHAT I'VE SHIPPED ▸ Migrated 250 million records from PostgreSQL to Snowflake for a B2B data platform. Monthly cost fell from $4,000 to $40, query time from 84 hours to 2. ▸ Built the dbt and Snowflake pipeline behind an influencer dashboard serving Nike, Under Armour, and New Balance. Adding New Balance lifted annual revenue by $1.8M. ▸ Rebuilt the reporting layer for a coaching platform with 100,000 users, taking dashboard refresh from 6 hours down to near real time. ▸ Scraped 10,000+ restaurant outlets across DoorDash, Uber Eats, and Grubhub at a 98%+ success rate, feeding clean order data into BigQuery. WHAT I DELIVER ✔ ETL and ELT pipelines (Python, SQL) ✔ Cloud data warehouses (Snowflake, BigQuery, Redshift) ✔ Data migration and data modelling ✔ dbt transformations and Airflow orchestration ✔ Data ingestion (Fivetran, API integration) ✔ Web scraping and data extraction at scale ✔ Cloud infrastructure (GCP, AWS, S3, Lambda) ✔ Analytics and BI-ready datasets 150 contracts and $200K+ earned here, Top Rated Plus. Google Certified Professional Data Engineer, master's from Georgia Tech on a Fulbright scholarship, 8 years across these stacks. Based in Abu Dhabi, working US and EU hours, and I reply inside 4 hours. If your warehouse is slow or the bill keeps climbing, send me two things: what you're running now and roughly what you pay a month. I'll send back a free audit showing where the cost and the time are going, and what I'd change first. Let's talk in chat.

Your data pipeline is either driving decisions, or quietly slowing your business down. Most teams don’t struggle with data volume, they struggle with reliability. Pipelines fail without alerts, dashboards lag behind reality, and engineers spend more time fixing than building. The result is slower decisions, growing technical debt, and missed opportunities. I design and build robust, scalable data systems that simply work, from ingestion to analytics-ready data. The focus is always on clarity, performance, and reliability, so your team can trust the data and move faster without constant firefighting. My tech stack: Python, SQL, Spark, Apache NiFi, Airflow, Kafka, Flink, Snowflake, BigQuery, AWS, GCP, Docker, Terraform, and FastAPI. If you share your current setup or challenge, I’ll break down exactly how to fix or scale it. I usually respond within a few hours.

I build cloud-native data platforms at scale. Lakehouses, streaming systems, AI infrastructure. My clients are companies where bad data or downtime isn't an option. I have 6 years of experience designing and delivering greenfield data architectures for Fortune 100 enterprises across fintech, healthcare, and identity management. Most recently, I migrated a fintech platform off a single monolithic server onto a distributed lakehouse on AWS with Spark, Airflow, and Apache Iceberg. That cut pipeline runtimes by 50% and infrastructure costs by 40%. Here's what I work with: Lakehouse & batch: Spark, PySpark, Databricks, Iceberg, Hudi, Trino, Airflow Streaming & CDC: Kafka, Debezium, ksqlDB, Spark Structured Streaming, ClickHouse Cloud: AWS (EKS, EMR, Glue, Lambda, SageMaker), Azure (AKS, Databricks, AI Foundry) Infrastructure: Kubernetes, Terraform, Helm, Docker, GitHub Actions CI/CD Governance: Apache Ranger, RBAC, row-level security, HIPAA/GDPR/SOC 2 AI/ML infra: RAG pipelines, vector databases (Pinecone), LLM-backed applications I've contributed to open-source projects like Debezium (Red Hat's CDC platform) and Mage.ai. I work independently, communicate proactively, and I'm used to collaborating remotely across US, EU, and Middle East time zones. If you need someone who can own the architecture end to end, not just write pipelines, let's talk.

⚡ Data pipelines breaking, slow, or too expensive? That’s exactly what I fix. I help companies turn messy, unreliable data systems into scalable, high-performance pipelines — so teams can actually trust their data and move faster. Over the past few years, I’ve built and optimized data platforms handling 50GB/day streams to 500GB+ processing workloads, across ecommerce, fintech, real estate, and SaaS. 🔑 What I Deliver (Real Outcomes, Not Just Tools) 🔹 Robust ETL/ELT Pipelines: Built on AWS Glue, Azure Data Factory, Databricks, Airflow → Designed for zero-failure execution & recovery-safe architecture 🔹 Cloud Data Warehousing: Redshift, Snowflake, BigQuery, Azure Synapse → Structured for fast queries, low cost, and scalable analytics 🔹 Real-Time & Batch Processing: PySpark, Kafka, Delta Lake → Handling millions of records daily with optimized latency 🔹 AI-Ready Data Foundations: → Clean, structured pipelines ready for ML models, LLMs, and BI tools. 🔹 Performance Optimization: → Reduced processing time by 50% on production workloads → Optimized queries & pipelines saving compute cost + runtime 🔹 Business Intelligence Layer: Power BI, Looker Studio → Dashboards that leadership teams actually use for decisions. 📦 Proven at Scale:- 🏪 Ecommerce Data Platform (1M+ stores | 500K customers): Built end-to-end ETL pipelines using AWS Glue Implemented incremental (delta) processing across multiple data sources Designed Redshift warehouse for high-volume analytics Delivered Power BI dashboards for business insights ✅ Enabled AI-driven personalization for 100K+ active users ✅ Reduced manual data dependency across teams 💳 FinTech / Freight Billing AI Platform: Designed data infrastructure for AP/AR automation Integrated ERP + TMS systems via APIs Built real-time dashboards + audit tracking ✅ Improved financial visibility & reconciliation accuracy ✅ Enabled automated decision-making for cash flow optimization 🏠 Real Estate Data Intelligence Platform: Built pipelines ingesting: Zillow Realtor HUD Census Crime datasets Centralized into PostgreSQL + Azure Data Warehouse ✅ Computed ROI, NOI, Cap Rate, Cash Flow at scale ✅ Enabled investment decision analytics 📊 Retail Location Intelligence (Databricks / Snowflake): Processed ~500GB mobile location data Built anomaly detection using moving averages ✅ Automated detection of: data drops duplicates false entries 🔧 Logistics Optimization (Azure): Replaced upsert with SQL MERGE ✅ Reduced processing time by 50% ✅ Achieved zero data loss in production pipelines 📡 Wellness Data Platform: Consolidated fragmented APIs into unified pipelines ✅ Fixed data failures on 250MB+ tables (~1GB compute load) ✅ Improved data consistency and pipeline reliability

I'm a Senior Data Engineer with 4.5+ years of experience building scalable, cloud-native data platforms that turn raw data into reliable, business-ready insights. I've delivered enterprise solutions across banking (NAB), healthcare (Molina), and CPG (PepsiCo), specializing in end-to-end pipeline architecture, data modeling, and cloud migrations. What I bring to your project: 🔹 Cloud Data Engineering – Deep expertise in Azure (Databricks, Data Factory, Synapse) and AWS (EMR, Glue, S3, RedShift), with hands-on migration experience from on-prem and Teradata to cloud. 🔹 Pipeline Architecture & ETL – I design and build robust ingestion frameworks handling batch, incremental, and real-time data (Event Hub, Kafka) across formats like JSON, CSV, Parquet, and fixed-width files. 🔹 Data Modeling & Warehousing – Skilled in dimensional modeling, Data Vault, star/snowflake schemas, and silver/gold layer design. I've modeled 50+ tables across Oracle Fusion, SAP S/4, and healthcare domains. 🔹 Transformation & Orchestration – I translate complex business rules into DBT models, orchestrate workflows with Apache Airflow or AutoSys, and automate CI/CD via Jenkins and Azure DevOps. 🔹 Performance & Governance – I tune PostgreSQL and Spark jobs, implement data quality checks, reconciliation frameworks, and ensure compliance with data governance standards. 🔹 Generative AI & MLOps – Databricks-certified in Generative AI, with experience integrating MLflow for experiment tracking and building LLM-based automation using OpenAI and LangChain. Tech Stack: Python | SQL | Scala | Apache Spark | DBT | PostgreSQL | Snowflake | Airflow | Databricks | Azure | AWS | Git | Jenkins | MLflow | Power BI Certifications: Databricks Certified Data Engineer Professional | Azure Data Engineer (DP-203) | Snowflake SnowPro Core | Fabric Analytics Engineer (DP-600) | Generative AI Engineer Associate Whether you need a production-grade pipeline, a cloud migration, or a well-modeled data warehouse, I deliver clean, documented, and scalable solutions — on time and with clear communication. Let's discuss your project!